Senior AI Engineer (Kubernetes & Customised Scheduler)

The role

The Senior AI Engineer will design, develop, and operate a proprietary Kubernetes-native job scheduling platform for AI workloads. This role focuses on building a workload orchestration layer for massive-scale AI factories using Model-to-Grid technology.

What you will do

• Design, build, operate, and improve the proprietary Kubernetes-native job scheduling platform.
• Develop scheduler architecture, custom resource definitions, controllers, and APIs.
• Implement scheduling policies for training, fine-tuning, inference, and other workloads.
• Build topology-aware and resource-aware placement mechanisms.
• Implement workload-control capabilities like admission control, queueing, and preemption.
• Develop workload demand-shaping and grid-aware controls.
• Integrate the scheduler with Kubernetes control-plane and various platform services.
• Build developer and user experience tools including APIs, SDKs, and CLI workflows.
• Enable Model-to-Grid benchmarking by exposing scheduler and workload data.
• Build observability into the scheduling platform.

Required qualifications

• 5+ years of experience in DevOps, site reliability engineering, platform engineering, distributed systems, cloud infrastructure, or comparable software and systems-engineering roles.
• Deep practical expertise in Kubernetes architecture and operations.
• Demonstrated experience building, extending, or deeply integrating workload schedulers, resource managers, or orchestration systems.
• Strong programming skills in Go.
• Python proficiency for automation, integration, tooling, and performance analysis.
• Experience building production Kubernetes controllers, operators, scheduling plugins, admission webhooks, REST or gRPC APIs, and CLI tools.
• Strong understanding of GPU-accelerated AI/ML workloads.
• Practical experience with GPU scheduling and resource allocation.
• Understanding of GPU system topology and high-performance AI networking.
• Familiarity with large-scale NVIDIA GPU architectures.
• Experience working with storage and data-path considerations for AI workloads.
• Understanding of AI-factory or data-center operations.

Nice to have

• Experience with Slurm, Slinky, vCluster, Kueue, KAI, Volcano, YuniKorn, or related systems.

About the company

Firmus Technologies is a global leader pioneering the development and operation of efficient AI infrastructure across Asia Pacific. They design, build, and operate the AI Factory using a model-to-grid technology approach.